Group Assignment

Load the libraries and the SA data

## Load the libs
# EDA
library(DataExplorer)

## Warning: package 'DataExplorer' was built under R version 4.1.2

library(ggplot2)

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library(plotly)

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# Core 
library(tidyverse)

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## v tidyr   1.2.0     v stringr 1.4.0
## v readr   2.1.2     v forcats 0.5.1
## v purrr   0.3.4

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library(timetk)

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library(lubridate)

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## Attaching package: 'lubridate'

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# Accuracy
library(yardstick)

## Warning: package 'yardstick' was built under R version 4.1.2

## For binary classification, the first factor level is assumed to be the event.
## Use the argument `event_level = "second"` to alter this as needed.

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# Anomaly
library(anomalize)

## Warning: package 'anomalize' was built under R version 4.1.3

## == Use anomalize to improve your Forecasts by 50%! =============================
## Business Science offers a 1-hour course - Lab #18: Time Series Anomaly Detection!
## </> Learn more at: https://university.business-science.io/p/learning-labs-pro </>

# 1.0 Read DATA -----
# consumption ----
sa_cons <- read.csv("sa/totaldemand_sa.csv")
## Convert Dt
sa_cons<-sa_cons%>%mutate(DATETIME = ymd_hms(DATETIME))

# temperature ----

temp_sa<-read.csv("sa/temprature_sa.csv")
## convert Dt
temp_sa<- temp_sa %>% mutate(DATETIME = ymd_hms(DATETIME))

# forecasted ----
forcst_sa <- read.csv("sa/forecastdemand_sa.csv") 
forcst_sa<- forcst_sa%>% mutate(DATETIME = ymd_hms(DATETIME),LASTCHANGED = ymd_hms(LASTCHANGED))

Plot the time series

sa_cons%>%plot_time_series(.date_var = DATETIME,.value = TOTALDEMAND,.interactive = F)

ACF and PACF

sa_cons%>%filter(DATETIME>="2015-01-01")%>%
  plot_acf_diagnostics(.date_var = DATETIME,.value = TOTALDEMAND,.lags = 0:17520)

## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.

Plot seasonal diagnostics

sa_cons%>%filter(DATETIME>="2015-01-01")%>%
  plot_seasonal_diagnostics(DATETIME, TOTALDEMAND,.feature_set = c("quarter","month.lbl"),.y_lab = "DEMAND")

sa_cons %>%filter(DATETIME>="2015-01-01")%>%
  plot_seasonal_diagnostics(DATETIME, TOTALDEMAND,.feature_set = c("hour","wday.lbl","week"),.y_lab = "DEMAND")